CU Rise Analytics Statistician Interview Questions and Answers

Q1. Tell me about yourself?
Ans. 
I am a data scientist with a background in statistics and machine learning, passionate about solving complex problems using data-driven approaches.
• Background in statistics and machine learning

• Experience in solving complex problems using data-driven approaches

• Passionate about leveraging data to drive insights and decision-making
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Q2. Describe in detail about one of my main project.
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Developed a predictive model for customer churn in a telecom company.
• Collected and cleaned customer data including usage patterns and demographics.

• Used machine learning algorithms such as logistic regression and random forest to build the model.

• Evaluated model performance using metrics like accuracy, precision, and recall.

• Implemented the model into the company's CRM system for real-time predictions.
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Q3. Few questions related to my projects.
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Q1. Questions on Basics python(Since i am fresher)
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Q1. Which GenAI projects I have worked on
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Q2. What is the context window in LLMs
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Context window in LLMs refers to the number of surrounding words considered when predicting the next word in a sequence.
• Context window helps LLMs capture dependencies between words in a sentence.

• A larger context window allows the model to consider more context but may lead to increased computational complexity.

• For example, in a context window of 2, the model considers 2 words before and 2 words after the target word fo
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Q3. What is top_k parameter
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top_k parameter is used to specify the number of top elements to be returned in a result set.
• top_k parameter is commonly used in machine learning algorithms to limit the number of predictions or recommendations.

• For example, in recommendation systems, setting top_k=5 will return the top 5 recommended items for a user.

• In natural language processing tasks, top_k can be used to limit the number of possible next words in a
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Q4. What are regex patterns in python
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Regex patterns in Python are sequences of characters that define a search pattern.
• Regex patterns are used for pattern matching and searching in strings.

• They are created using the 're' module in Python.

• Examples of regex patterns include searching for email addresses, phone numbers, or specific words in a text.
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Q5. What are iterators and tuples
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Iterators are objects that allow iteration over a sequence of elements. Tuples are immutable sequences of elements.
• Iterators are used to loop through elements in a collection, like lists or dictionaries

• Tuples are similar to lists but are immutable, meaning their elements cannot be changed

• Example of iterator: for item in list: print(item)

• Example of tuple: my_tuple = (1, 2, 3)
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Q6. Do I have REST API experience
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Yes, I have experience working with REST APIs in various projects.
• Developed RESTful APIs using Python Flask framework

• Consumed REST APIs in data analysis projects using requests library

• Used Postman for testing and debugging REST APIs
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SQL and business related questions

Q1. Questions from SQL moderate level
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Q2. Que from python, power bi and resume
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Business related questions and graphs and metrics

Q1. Deep questions about Machine learning, Deep learning, Generative AI, the working of transformers etc.
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Q1. Deep questions about Machine learning and deep learning with projects done. This was a client round.
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Q1. Salary discussion, project discussion, why change? Why Wipro
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Q1. About Machine learning basics, activation functions linear regression, cnn, all basics..
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Q2. About project questions, about sdlc basic 3 questions
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Q3. About Why not used another model for training?
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Q1. Complex sql question based on lead lag
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Q2. Question based on data index
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Q3. Question based on data engineer
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Q1. What is stemming and lematization
Ans. 
Stemming and lemmatization are techniques used in natural language processing to reduce words to their base or root form.
• Stemming is a process of reducing words to their base form by removing suffixes.

• Lemmatization is a process of reducing words to their base form by considering the context and part of speech.

• Stemming is faster but may not always produce a valid word, while lemmatization is slower but produces valid wo...read more
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Q2. Python coding about default values in class and funtions
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Q3. Kmeans clustering evaluation
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Q4. How to measure multicollinearity
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Multicollinearity can be measured using correlation matrix, variance inflation factor (VIF), or eigenvalues.
• Calculate the correlation matrix to identify highly correlated variables.

• Use the variance inflation factor (VIF) to quantify the extent of multicollinearity.

• Check for high eigenvalues in the correlation matrix, indicating multicollinearity.

• Consider using dimensionality reduction techniques like principal componen
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Numpy,pandas,data analysis